Automation is reshaping how companies track employee performance and recognize achievements. From real-time metrics to milestone reminders, technology now supports processes that were once manual and inconsistent.

Managers have more visibility into progress, and employees receive clearer feedback and more timely recognition. Both performance tracking and appreciation are becoming continuous parts of everyday work instead of isolated events.

Continuous Tracking is Replacing Annual Reviews

Automation tools are now able to collect performance data throughout the year instead of storing it up for one formal conversation. Goal progress, project timelines, and productivity trends are visible in real time.

Also, TechRadar highlights how many managers are now using AI to draft or refine performance reviews. Faster documentation reduces forgotten achievements and creates more structured, evidence-based evaluations.

Ongoing tracking minimizes recency bias. Early wins remain visible, which supports fairer ratings and more balanced conversations about growth.

Recognition is Being Triggered by Performance Data

Recognition traditionally depended on managers noticing accomplishments and acting on them. Automation now connects performance metrics directly to recognition workflows.

Many leaders struggle to tie recognition efforts to measurable business outcomes. When recognition lacks structure, employees often experience appreciation as inconsistent or delayed.

Automation changes that dynamic by prompting recognition when predefined goals are achieved. Sales targets, service benchmarks, innovation milestones, and collaboration metrics can trigger alerts that prompt timely acknowledgment.

Recognition becomes more consistent because it is supported by systems rather than memory alone. Employees gain clearer connections between performance and appreciation.

Milestone Recognition is Becoming Simpler

Service anniversaries and tenure celebrations often rely on spreadsheets or manual reminders. Automation now flags milestone dates automatically and routes notifications to the right stakeholders.

So, automated workflows ensure reminders are sent in advance, budgets are approved on time, and recognition does not fall through the cracks.

In turn, it is much easier for leaders to hand out more personalized employee anniversary gifts like awards, vases, and decorative gavels from Successories to honor years of service.

Consistency builds credibility. Employees who see milestone recognition delivered on time are more likely to feel valued and respected.

Performance Metrics Are Expanding

Automation is also redefining what strong performance looks like. Output alone is no longer the only measure.

Seeing as there is a shift toward human-machine performance models, leaders are increasingly redesigning roles as employees move toward collaborating with AI-driven systems.

Performance discussions now include adaptability, digital fluency, and the ability to work effectively alongside automated tools. Automation makes these capabilities measurable, giving HR teams better insight into evolving skill sets.

Employees who embrace technology often demonstrate stronger efficiency and problem-solving capacity. Performance tracking systems are adapting to reflect that reality.

Peer Recognition is Becoming Scalable and Transparent

Automation platforms are also making peer-to-peer recognition easier to manage across larger or distributed teams. Digital acknowledgments can be tracked, categorized, and analyzed over time.

Centralized recognition data increases visibility across departments. Contributions that might otherwise go unnoticed gain exposure through shared platforms.

Leaders benefit from engagement insights drawn from recognition activity. Patterns in peer appreciation can reveal collaboration strengths and cultural gaps that require attention.

Automation Means Smarter Tracking and Recognition

Automation is changing employee performance tracking and recognition by making both more structured and consistent. Real-time metrics clarify expectations, and performance-based triggers ensure achievements and milestones are acknowledged.

Organizations that balance automation with thoughtful appreciation build stronger workplace cultures.

If your team is evaluating how automation is changing employee performance tracking and recognition, review your current systems and explore ways to align performance data with rewards that genuinely resonate with employees.

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Your HR tech stack looks solid on paper. Engagement surveys, performance reviews, recognition, development plans, and an HRIS holding it all together. Every box checked.

But here’s the catch: each of those tools works fine on its own. The trouble starts in between them. A dip in engagement never makes it to the manager having the 1:1. A high performer’s recognition trails off, and nothing connects that quiet shift to the retention risk building underneath it. The signals are all there. They’re just never in the same room together.

That’s the disconnected data problem, and it’s worth naming clearly, because it’s not just an IT headache. It shapes how confidently you can answer your CHRO’s questions, how early you catch a flight risk, and how much of your week goes to piecing together reports instead of acting on what they say.

The good news? You don’t need more data, and you don’t need to rip out your tools. You need them to finally talk to each other. Here’s what disconnected data is quietly costing you, and what happens when it connects.

What do we mean by disconnected people data?

Disconnected people data is what happens when your engagement surveys, performance reviews, recognition activity, development plans, and HRIS records all live in separate systems that were never built to talk to each other. Each one holds a piece of the story, but no one system shows you the whole picture. So instead of one clear view of how your teams are really doing, you end up with scattered snapshots, and it’s on you to stitch them together into something you can actually act on.

The real cost of fragmented people data

Disconnected people data costs organizations millions a year in lost productivity, avoidable turnover, and missed talent. A mid-size company of 2,500 employees can lose nearly $4 million annually to underequipped managers and unretained top performers alone.

If you’ve ever pulled reports from five different systems just to answer one question from your executive team, you know this feeling well. You know the answer is in there somewhere. You just can’t get to it cleanly, quickly, or with the confidence you need to act on it.

That gap isn’t a personal failing, and it isn’t a technology glitch either. Research from Quantum Workplace shows HR leaders typically use between two and four HR solutions from different providers, but only 39% say those systems are usefully integrated. The data keeps piling up. The clarity doesn’t follow.

Here’s where fragmented data shows up most:

1. The cost of underequipped managers

Managers are your strongest lever for engagement and performance. When they thrive, their teams thrive right along with them. When they struggle, that struggle spreads to everyone they lead.

Most managers aren’t struggling because they lack talent. They’re struggling because no one gave them what they needed to succeed. The average manager carries 51% more responsibility than they can realistically handle, and 39% have never received formal leadership training.

The business impact is real. Managers in the top decile of leadership effectiveness generate twice the net revenue of everyone else. That gap between your strongest and weakest managers isn’t just a development issue—it’s a performance problem sitting quietly inside your organization right now.

2. The cost of overlooking high-potential talent 

High performers aren’t just valuable. They’re disproportionately valuable, generating 400 to 800% of what an average employee produces. Yet only 57% of organizations have a formal process for identifying who those people actually are.

Without connected data, talent identification often comes down to visibility and advocacy: who gets nominated, who gets noticed, who has the right people in their corner. That means the employees generating the most impact aren’t always the ones who make the list. Every quarter they go unrecognized is a quarter of outsized contribution your organization never gets to fully use.

3. The cost of missing retention risk

By the time a high performer resigns, the decision was usually made months earlier. The warning signs were there: a dip in survey sentiment, development conversations that quietly stopped, recognition that dried up. On their own, none of these signals is loud enough to raise a flag. Together, they tell a clear story.

Replacing an employee costs 50 to 200% of their annual salary, and according to our research, one in three departing employees say their exit was preventable. Those signals already exist in your data. They’re just scattered across systems that were never built to talk to each other.

What these costs adds up to

Picture an organization with 2,500 employees and 250 managers. If 50 of those managers are underequipped and 10% of their direct reports disengage as a result, you’re looking at $1.7 million in lost productivity a year.

Now add retention risk to the mix. If 10 high performers leave because their impact went unseen, replacing them at twice their salary adds another $2 million.

That’s nearly $4 million hiding in disconnected data, and it doesn’t even account for what happens when teams don’t have the conditions to thrive: effort pointed in the wrong direction, skills that develop too slowly, potential that never gets recognized.

What changes when the data connects

For years, connecting signals across fragmented systems took more time and resources than most HR teams could reasonably spare. That’s changing. AI can now work continuously across engagement, performance, development, recognition, and HRIS data, comparing it against benchmarks and surfacing patterns that used to be invisible.

The shift isn’t about collecting more data. It’s about finally hearing what your existing data has been trying to tell you.

When these signals connect, you can start answering the questions that keep HR and executive leaders up at night:

• Which managers are set up to succeed, and which need more support?
• Which teams have a high concentration of top talent, and where is there room to grow it?
• Who is at risk of leaving, and could still be retained?
• Where is burnout quietly building before it shows up as attrition?
• Is your development investment actually keeping people, or just checking a box?

Turn fragmented data into connected talent insights

Seeing the full picture only matters if it changes what you do next. That’s where Quantum Workplace comes in.

Quantum Workplace connects engagement, performance, development, and recognition signals into one unified view, so leaders stop piecing together fragmented snapshots and start seeing what is actually happening across their teams. AI-powered insights surface the patterns hiding between your systems, and every finding comes with a clear, prioritized next step, framed by your organization’s own priorities and benchmarks, and delivered to leaders right in the flow of their work.

Connected data helps your HR team:

• Build better managers by giving leaders at every level, not just HR and executives, the timely, practical insight they need to coach, develop, and lead with confidence.
• Identify top talent by understanding who is actually driving impact across your organization, so you invest where it matters most instead of relying on who gets noticed.
• Keep your best people by spotting engagement and retention risks early, before a resignation ever hits your desk.

Loons, gulls, puffins, and petrels are some of the 100 species of birds that can both fly and swim. These diving birds can plunge in water to swim after prey, and leap back into the air to fly away. 

Inspired by these naturally aquatic aviators, engineers at MIT and EPFL in Lausanne, Switzerland, have designed a robot that can swim underwater, then flap out of the water to continue flying through air, much like diving birds. 

The “flapping-wing aerial-aquatic vehicle,” or FAAV, weighs less than 300 grams (about half a pound) and is designed to help scientists study the mechanics that enable diving birds to fly through air and water. 

The robot has a central body, or fuselage; two flexible, flapping wings; and a steerable tail. The wings and tail can be swapped out for different sizes. In experiments carried out in a water tank and at a local lake, the engineers identified combinations of wing size, flapping frequency, and tail angle that enable the robot to smoothly transition from swimming through water to breaking through the surface to flying through the air.

Their results, which appear today in the journal Science, could help scientists understand how diving birds adapt their flight mechanics to move through air and water — mediums with very different physical properties. The design could also launch a new class of aerial-aquatic drones and vehicles. The researchers envision such winged robots could be deployed in oceanography to fly to and sample from aquatic regions that would otherwise be too dangerous for traditional ocean vessels to access.

“Our dream vision is for oceanographers, marine biologists, and members of coastal communities to launch this robot from a boat, or from shore, and it would fly close to the area of interest, such as an iceberg or a port facility, or over a pod of whales,” says Raphael Zufferey, assistant professor of mechanical engineering at MIT. “It would dive into the water to take a measurement or collect a sample, and fly back to deliver the data at a fraction of the cost of traditional methods. Then it could go back out to dive for more.” 

Zufferey is the lead author of the new study, which includes co-authors from EPFL and Northwest Indian College in Bellingham, Washington.

Flight mechanics

At MIT, Zufferey heads up the AURA Lab, where he and his students engineer aerial and aquatic vehicles inspired by biomechanics in nature. The robots they build are small in size and designed to unobtrusively explore and monitor the health of oceans and waterways. 

For their new work, the team aimed to design a vehicle that can fly in the air and underwater. Any such vehicle would have to adapt to and transition between two very different substances. Water is 1,000 times denser than air, and moving through one or the other requires very different mechanics. Or so people might assume.

“You have to do some adaptation to make that transition work. But there’s a solution that exists in nature,” Zufferey says. “Birds like puffins can fly very fast through the air, and can dive and swim through water at speeds of 3 meters per second. They’re able to do pretty amazing things. So we knew is was possible. Just no one had tried this in a mobile robotic system.”

To get an idea for how diving birds fly, the team looked through the scientific literature and pulled together available data on puffins, petrels, kingfishers, and other diving birds. They observed that smaller birds flap their wings around 10 times per second when flying through air, and around four times per second when swimming through water. Larger birds have a slightly lower flapping frequency through both air and water due to their wider wingspans. 

With the biomechanics of birds in mind, the team developed a winged robot designed to flap at similar frequencies to that of actual diving birds. 

Making the leap

The new robot roughly resembles a bird, with a body, two wings, and a tail. The body contains a battery and waterproof electric motor that drives a crankshaft, which in turn pumps the wings up and down at preset frequencies. The wings are made of thin membranes that are coated with hydrophobic nanoparticles to help wick away water. And the tail is motorized, enabling it to change its angle to help the robot fly up or dive down. 

The wings can be swapped out for different sizes. The researchers fabricated and tested three sets of wings: small (60 centimeters wide), medium (80 centimeters), and large (100 centimeters). They carried out experiments first in a small water tank, then in Lake Geneva in Switzerland.

In their tests, they placed the robot underwater, about half a meter below the surface. They programmed the wings to flap at certain frequencies and the tail to pitch at certain angles throughout the robot’s flight. They then observed under what conditions the robot successfully swam up toward the surface, out of the water and into the air. 

The robot flew multiple flights with different wing sizes, flapping frequencies, and tail angles. Overall, the team found the robot was able to reliably fly, swim, and transition between water and air when it flew with medium-sized wings. Flexibility in the wings is key; the wings need to be flexible enough to minimize flapping amplitude in water and also firm enough to keep the robot aloft in the air. 

The researchers also found the robot could swim through water at speeds of almost 1 meter per second when it flapped with a frequency of around 5 herz, or five flaps per second. The robot could fly through the air at around 6 meters per second, when flapping at a similar frequency. The speeds and flapping frequencies of the robot were similar to that of actual diving birds. 

To make the leap from water to air, they found the robot should be pitched at 70 degrees — a relatively steep angle that keeps the robot’s wingtips from touching the water’s surface as it flaps up and into the air. Any steeper, and the robot would tip back into the water.

Interestingly, this combination of wing size, flap frequency, and tail pitch enabled the robot to swim underwater, launch off the surface, and fly, without something that many diving birds require: feet. When birds such as puffins and ducks take off from the water’s surface, they paddle their feet, along with flapping their wings and pitching their tails. Surprisingly, Zufferey and his colleagues found that, at least in robotics, the act of flying out of water doesn’t necessarily require a paddling maneuver. 

“If you look at birds, most birds need to paddle at the surface to take off. And the question was, do we need the same for robots? And it turns out we don’t,” Zufferey says.

Going forward, the team is improving the design of the wings to enable them to turn in addition to flapping up and down. They will also test the robot’s performance under turbulent conditions, such as swimming out of choppy waters and flying through wind. Then, they hope to deploy the vehicle to help answer questions in ocean science.

“One of the major challenges in ocean science is collecting data both frequently and across many locations, which is something this robot could do in the future,” Zufferey says. “You could send this out not just every week, but every hour. It could fly out at high speeds, dive in fly back, deliver its data, and go back out, multiple times.”

This work was supported, in part, by a Marie Skłodowska-Curie Actions fellowship grant.

Most people think of the waterfront as the edge of the city. A team of MIT researchers sees it as a dynamic, Lego-like construction site.

Their new system, called “FloatForm,” is a swarm of small square robotic boats that assemble themselves into larger structures on the water, break apart, and reassemble into something new, all with minimal human direction. 

Each robot, about the size of a dinner plate at 21 centimeters square, is a self-contained vessel with its own thrusters, sensors, and magnetic latches. Together, they hint at a future in which floating infrastructure could become more adaptive: a temporary platform after an emergency, a market on a canal, or a stage that appears for a festival and dissolves when the crowd goes home.

“Our FloatForm projects envisions a future where the waterfront becomes a programmable extension of the city, where autonomous boats can self-organize into bridges, platforms, and other useful structures on demand,” says Daniela Rus, the Panasonic Professor of Electrical Engineering and Computer Science at MIT and director of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). “This kind of distributed robotics opens new possibilities for mobility, emergency response, public space, and infrastructure on water.”

“With FloatForm, we are essentially turning static water surfaces into dynamic, programmable spaces,” says Wei Wang, lead author of a new paper on the project and a former MIT research scientist who now leads the Marine Robotics Lab at the University of Wisconsin at Madison. “Imagine an urban environment where public space isn’t fixed, but can autonomously expand, contract, or reconfigure on demand.” 

“We see it as forming infrastructure on the water, using a modular system to create one larger system,” says Alejandro Gonzalez-Garcia, a former researcher with MIT CSAIL and the Senseable City Lab. “If there’s an emergency, you could form a new bridge to alleviate traffic in the city. Or you could create floating markets and floating stages. If you want a more livable city, you want to use the water, too.”

The open-access work, published today in Nature Communications, comes from the labs of Rus and Carlo Ratti, professor of practice of urban technologies and planning at MIT and director of the Senseable City Lab, and grows out of Roboat, their joint project with the Amsterdam Institute for Advanced Metropolitan Solutions that put full-size autonomous vessels on Amsterdam’s canals. Those canals once carried the city’s goods; today, they mostly carry tourists. 

“We explored whether the canals could be used for waste collection, or for transport, to offload some of the stress on the roads back onto the water,” says Niklas Hagemann, an MIT graduate student in architecture, CSAIL affiliate, and former Senseable City Lab researcher who has worked on the project since its early stages. “Urban areas are getting denser, so could you expand public space onto water that’s currently underutilized?”

FloatForm shrinks that vision down to tabletop scale to answer a harder question: How do you get dozens, and eventually thousands, of floating robots to organize themselves?

Lessons from the ant raft

The team found its answer in biology. Fire ants famously survive floods by linking their bodies into living rafts, with no leader choreographing the assembly. Each ant follows simple local rules, and a resilient structure emerges.

“Each ant is an independent agent,” says Gonzalez-Garcia. “We wanted each robot to have its own capabilities, the same way ant colonies form a raft.”

Most existing self-assembling robot systems, on water and elsewhere, rely on a central computer dictating every move. That approach is vulnerable to single points of failure and scales poorly: The planning math balloons as robots are added, and the swarm must assemble sequentially, with most robots idling while they wait their turn. FloatForm flips the balance. A lightweight central planner steps in only sparingly, assigning each robot a final position to perfect the lattice, a level of geometric precision that purely distributed methods struggle to guarantee. Everything else, including navigating toward the target shape, avoiding collisions, and adapting to disturbances, runs on the robots themselves, which coordinate by exchanging positions with their immediate neighbors. The whole swarm moves at once.

That parallelism is what sets the work apart. The planning complexity of FloatForms approach depends only on a robot’s local neighbors, not the total size of the swarm. “What we’re trying to do is to have minimal central intervention, and have them all move together at the same time,” says Gonzalez-Garcia.

In experiments at MIT, a fleet of eight robots repeatedly gathered from random positions into a target shape, latched into a rigid structure, broke apart on command, reassembled into a new configuration, and then drove across the pool as a single vessel, with each run taking four to eight minutes. In that final mode, called collective transport, a planner charts a trajectory for the whole structure and each robot computes its own contribution. “Every robot becomes an actuator,” Gonzalez-Garcia explains. Simulations showed the framework scaling smoothly to swarms of 64.

“The beauty of this largely decentralized approach is that the computation doesn’t get bogged down as the swarm grows,” says Wang. “Whether you are working with eight boats or 80, the entire fleet coordinates and moves simultaneously. Because the overall assembly time doesn’t significantly increase in principle, the system remains highly scalable.” 

There’s a physical payoff to sticking together, too. “Our boats become more stable by joining together, like the ant raft, if you have waves or currents,” Hagemann says.

An origami handshake

The robots connect through a latching mechanism hidden entirely inside each hull. A single servo motor at the center drives an origami-inspired auxetic structure, a geometry that contracts uniformly in all directions at once, pulling permanent magnets on all four sides inward to release, or pushing them outward to grab a neighbor across gaps of 10 to 15 centimeters. The magnets are arranged with alternating polarities, so the boats reliably click into clean square lattices.

The elegant part is what the mechanism doesn’t do: consume (much) power. A 3D-printed gearbox holds the latch in either state with the motor switched off. “It uses energy to latch and de-latch, but in between those states, it doesn’t use any energy,” says Hagemann. For infrastructure that might hold a configuration for hours, that matters. “Because the robots are so small, you can only have a battery so big,” adds Gonzalez-Garcia. “If they use less energy on latching, they can use more on computation, or on actually moving.”

Getting there took some humbling engineering. Four miniature thrusters arranged in an “X” give each robot omnidirectional motion, including turning in place, but they pack large forces relative to the robots’ tiny inertia, which made early prototypes twitchy and prone to aggressive spins at low speeds. The team added stabilizing fins to increase hydrodynamic drag and tuned the controllers to stay robust across robots that, at this scale, are never quite identical. The magnets posed their own problem: They held on so well that de-latching sometimes required the robots to twist themselves free.

From the tank to the canal

Across 10 trials, the system completed its missions without human intervention 90 percent of the time with four robots and 70 percent with eight. When things did go wrong, the architecture showed its resilience: A robot that briefly lost its bearings could rejoin the structure on its own, without bringing the whole swarm to a halt, and robots stuck in formation deadlocks learned to shake themselves free and retry.

Moving from a controlled indoor tank to a real canal or harbor will take more than confidence. “There’s always a relationship between the size of a boat and the magnitude of the disturbance it can handle,” says Gonzalez-Garcia. “These boats are very small, so in very disturbed water, they cannot work.” Scaling up will mean reinforcing the latches, potentially with mechanical interlocking like the full-size Roboat used, and trading the lab’s ultrasonic indoor positioning for GPS or vision-based sensing. Helpfully, the coordination algorithm was designed to be sensor-agnostic: swap the sensors, keep the logic.

The team envisions applications well beyond city canals, from forming temporary platforms for offshore inspection and maintenance to adaptive sensor networks for studying migratory species to reconfigurable docking stations for emergency response in hard-to-reach areas. There is also potential for offshore and remote operations, from temporary construction platforms to environmental monitoring and scientific expeditions.

And the geography is wide open. “Venice, the Netherlands, Belgium, the fjords and lakes of Norway, really any city with a river can take advantage of this,” says Gonzalez-Garcia. “The project uses spaces where water is already important, but it also raises the question: Where else can water be used for something more?” 

“This is an exciting step forward in realizing distributed collective behaviors on water,” says University of Michigan Assistant Professor Steven Ceron, who wasn’t involved in the research. “Assembly, self-reconfiguration, and collective motion are difficult enough in dry environments, but achieving these behaviors in a predominantly distributed fashion on water represents a serious additional challenge, and this team has credibly overcome it. By shifting the computational burden onto the robots themselves, they have built a more resilient system that in the near future could enable robot collectives like this to be deployed in open-water environments for search operations, environmental monitoring, and reconfigurable marine infrastructure.”

Gonzalez-Garcia, Hagemann, and Wang wrote the paper with senior authors Ratti, who is also a professor at Politecnico di Milano, and Rus. Gonzalez-Garcia is additionally affiliated with the MECO Research Team at KU Leuven. The research was supported by a grant from the Amsterdam Institute for Advanced Metropolitan Solutions, with additional support from the University of Wisconsin at Madison. The team thanks MIT Sea Grant and Professor Michael Triantafyllou for providing the test tank.

Parking demand rarely moves in a straight line. It rises before events, dips during slow workdays, and changes again by weather, access, and nearby activity. Many operators still price spaces as if every hour carries equal value.

That approach leaves income uncollected and labor stretched thin. Better tools give owners clearer visibility, tighter controls, and practical ways to manage parking as a revenue-producing asset.

Revenue Gaps

Revenue leaks often start with guesswork. A busy lot can undercharge during peak demand, while a slow site may price drivers out.

A parking management system connects pricing, access, payment, and enforcement to actual usage data, helping operators capture fair income from each space without placing more staff at the curb.

Smarter Pricing

Pricing should reflect pressure on the lot, not habit. Commuters, restaurant guests, airport travelers, and event crowds all create different demand patterns.

One fixed rate treats those visits the same. Flexible rules let managers lift prices during high-use periods and reduce fees when occupancy softens. That balance improves yield while keeping spaces in active use.

Faster Payments

Payment friction costs money. Drivers may skip payment when kiosks are slow, cash is required, or instructions feel unclear. Mobile checkout, text links, and quick-response codes shorten the process.

Each transaction can connect plate data, time, and card details in one record. Cleaner payment trails reduce disputes and make reconciliation less painful.

Lower Hardware Costs

Heavy equipment adds hidden expense. Gates, ticket printers, kiosks, and cash boxes need servicing, parts, inspections, and eventual replacement. A stalled machine can create lines within minutes.

Mobile-led payment reduces dependence on fixed hardware and shifts routine work away from staff. Less equipment exposure means fewer breakdowns, fewer service calls, and stronger net income.

Leaner Staffing

Labor should support judgment, not repetitive collection. Manual payment handling requires hiring, scheduling, oversight, cash counts, and security controls. Busy periods make those duties harder to predict.

Digital payment and remote monitoring allow attendants to focus on exceptions, customer support, and enforcement review. Staffing becomes more precise, which protects margins during both quiet and crowded periods.

Better Enforcement

Unpaid parking often appears small until monthly totals are reviewed. Plate recognition, time records, photos, and expiration alerts make enforcement consistent. Drivers can receive reminders before their time expires, which encourages them to pay for extensions.

If a violation occurs, staff have documented evidence for follow-up. That record reduces arguments and keeps enforcement from feeling arbitrary.

Real-Time Control

Delayed reports force managers to react after they have already lost revenue. Live dashboards show occupancy, payments, permits, rates, violations, and location performance as activity changes.

A manager can adjust pricing, check an event result, or compare sites without waiting for manual spreadsheets. Faster information leads to faster corrections, especially during short demand windows.

Event Revenue

Events compress parking income into a few intense hours. Poor planning can leave money uncollected before the first guest arrives. Pre-sold passes, temporary rates, and digital entry records help operators prepare for demand.

Staffing can be scheduled with better confidence, and less cash must be handled after closing. Clear records also simplify partner settlements.

Property Value

Parking performance can affect a property’s financial profile. A lot once treated as a support function may become a stronger income source after payment, pricing, and enforcement improve.

Higher net operating income can support better asset valuation. Tenants also gain from easier permits, guest access, and validation controls that reduce front-desk interruptions.

Reporting Discipline

Good reporting changes how decisions are made. Daily revenue, average ticket value, occupancy, refunds, violations, permits, and event trends all tell a useful story.

Managers can test rate changes, spot enforcement gaps, and compare seasonal behavior. Clear numbers replace assumptions. Over time, that discipline helps each location become more accountable and easier to improve.

Customer Experience

Better revenue control should still feel fair to drivers. Clear signs, simple payment steps, accurate permits, and timely reminders reduce confusion. People are more likely to pay when the process is easy to complete.

Fewer complaints also lower support workload for staff. A better visit supports compliance, repeat use, and trust in posted rules.

Scalable Operations

Growth becomes harder when every location uses separate tools. Multi-site operators need consistent controls for rates, roles, reporting, permits, and enforcement while allowing local adjustments.

A shared platform helps managers compare performance across properties and train staff with less friction. New lots can be added with fewer administrative tasks and clearer operating standards.

Conclusion

A stronger parking operation starts with better visibility and disciplined control. Owners can raise income from existing spaces while reducing equipment, staffing, and administrative waste. Pricing should match demand, payment should be easy, and enforcement should rely on clear records.

Reporting then turns daily activity into useful business guidance. When these pieces work together, parking becomes a measurable asset instead of an overlooked cost center.

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It might seem a little strange to be dedicating a special issue of Futures of Work to research methods. But all contributors to this issue are dealing with work, and most of them focus on vulnerable or precarious workers, such as those with experiences of modern slavery, those who require multiple jobs to survive or – more broadly speaking – women and menopausal workers. Yet they also discuss a range of new(-ish) approaches and innovations in research methods in the process.

The reason for this is that, as all the articles make clear, we have not yet got right what we are looking at, how we look at these issues and, as a result, what data and evidence we produce. The shortcomings of existing data and surveys are often based on definitional inaccuracies and concerns with measurement. As Darja puts it, we are often imprecise about how many, who and why. This means that, to use Jennifer’s word, there are significant blind spots (in knowing when women work, for example) and, to paraphrase Luis, much of the data available to us is retrospective rather than forward-looking and focused on prevention. This Futures of Work issue therefore argues, overall, for the development of methods and ways of looking at work and labour market issues that produce significant and new insights.

There are further similarities between the articles. Most of them raise ethical questions or draw attention to differences in values and perspectives. Chris makes this especially clear by looking at how to involve those with lived experience of modern slavery in research without re-exploiting or retraumatising them, and without survivors’ roles becoming tokenistic. For some sensitive subject matters, it might be easier to reveal perceptions, understandings and discourses on them by depersonalising the research, as is done in the story completion approach that Victoria, Kara, Gemma and Vanessa discuss. In drawing attention to the impact of employing different methods, our contributors also highlight the responsibilities of researchers to be fair, whether this is in their representation of research participants or contributors, or in terms of how research can or should be used to influence policy and stakeholders in the field.

This issue includes three contributions from a quantitative starting point and two that would be labelled as qualitative. Yet it is also clear in the call for more mixed methods research (e.g. by Darja and Luis) that broad perspectives and trends are as important as the individual worker perspective, with the former often addressing questions such as how many, who, when and where, while in-depth qualitative work is often required to explain why. In many ways, therefore, ‘old’ and established principles in research methods are still maintained. This may not always be the case as new research methods emerge and existing ones continue to be developed.

If this issue highlights one thing, it is that there is always a broad range of considerations and options in how we undertake research, and also always more to learn on how we improve and further develop the implementation of the methods we use. Research methods play a significant role in how the future of work may play out because methodological choices are foundational to the type of knowledge that is developed of the issues workers face and how they might be addressed. So much so, that methods may be an issue we need to return to in forthcoming issues of Futures of Work.

Image credit: Luca Bravo via Unsplash

The issue

Multiple job holding has generated a growing body of literature examining who is working in more than one job and why, especially whether it is due to constraints or opportunities. Hence, whether multiple job holding is regarded as a widespread or small-scale phenomenon in contemporary labour markets depends on how it is defined and measured, and the data sources used.

Researchers who work with large-scale secondary quantitative data often have to adapt their research questions and variable definitions to the available data. However, when it comes to multiple job holding, critical reflections on definitions, data capture and methodological limitations remain surprisingly limited. We argue that these issues warrant greater attention, particularly given that the policy implications of multiple job holding remain inconsistent and sometimes contradictory. This lack of clarity may explain why multiple job holding has not been included in standard labour market analysis.

How many, why and who?

Estimates of the prevalence of multiple job holding vary greatly across studies, leading to divergent answers to the question of ‘how many’ workers multi job. For example, official labour market statistics in the USA and Australia suggest that around five to six per cent of the workforce hold more than one job. Another study instead finds that a much higher level (18 per cent) of US households have members with second earnings.

Findings on ‘why’, on the motivations of multiple job holding, also differ substantially from not having a choice, due to bad jobs that do not provide enough hours and pay, to more positive motivations including enjoyment, skill development or career enhancement. There is also disagreement about ‘who’ engages in multiple job holding. Some studies point to a greater need among women to take on additional jobs, while others find that in many countries in Europe, young men are more likely than young women to work in more than one job at the same time.

The differing interpretations of how many people engage in multiple job holding, why they do so and who is involved are all closely linked to how multiple job holding is measured, specifically who is included and who is not. Often also described as ‘moonlighting’ or ‘side hustles’, such definitions of multiple job holding usually include some form of self-employed activity. Using a definition of multiple job holding that includes self-employed activity is more likely to capture people motivated by enjoyment or dissatisfaction with their first job, similar to the motivations of becoming self-employed in general. In this context, concepts such as ‘portfolio’ or ‘mosaic’ careers, emphasising the more positive connotations of agency over one’s working career, may be more suitable for the ‘side hustler’ or ‘hybrid entrepreneurs’ who experiment with self-employment activities while retaining a less fulfilling job, at least in the short term.

Second or multiple job holding is also defined as having more than one concurrent job as an employee. When multi job holding is conceptualised this way, we are more likely to find people who take on additional jobs because they cannot find a suitable full-time job, which is especially relevant for women.

Beyond these definitional variations, we know little about how people respond to survey questions on whether they held a second job during a given reference period (typically the previous week). Measurement may be affected by under-reporting as some workers might be reluctant to report certain ‘side hustles’, for example due to concerns about disclosure in governmental surveys. They may be more likely to report more stable additional jobs such as regular part-time work. There are likely to be group biases in who reports an additional job or not.

Is administrative data useful for multiple job holding?

In 2024, the UK Office for National Statistics added to their data resources a linked administrative dataset combining the Annual Survey of Hours and Earnings (ASHE) with real-time payroll data from the tax authorities (HM Revenue and Customs). ASHE is an annual employer-completed survey of employees, providing highly accurate information on pay and working hours. However, personal information is limited (to gender and age) and there is the caveat that large employers are more likely than small employers to respond to the survey. In addition, as with many social surveys, ASHE has a reference period so that we don’t know anything about the employees outside its narrow window of time.

The payroll data that can be linked to ASHE are a collection of employees’ payslips from HMRC’s Pay As You Earn (PAYE) system. Employers are required to use PAYE for workers above a certain earnings threshold, as well as for those receiving state benefits or holding a second job. On their own, these administrative payslip data contain limited information beyond earnings. However, when linked with ASHE, they become more meaningful, allowing, for example, a gender analysis of detailed pay and employment.

All payslips per person in a tax year should be in the data, allowing the identification of multiple payslips on a weekly basis. In our analysis, we exclude multiple payslips from the same employer (these could be bonus payments) and any payslips that are related to pensions or a zero amount. Concurrent payslips may not be, due to distinctly different jobs, such as the earnings academics receive from external examining. Among high-skilled occupations, it is therefore likely that multiple payslips are found more often in administrative than in survey data. We therefore propose the term ‘multi-payroll’ to describe this situation. This measure captures formal, recorded employment activities, but administrative data do not capture informal work. In addition, since self-employment activities are not covered in PAYE data, this measure is not suitable for studying the gig economy.

Despite limitations, payroll administrative data offer a valuable tool for examining multiple earnings from paid employment, reducing concerns about group biases in recording employment status (for example, gender differences). However, a key downside is the absence of subjective information, specifically, how workers think about their activities. Are these viewed as two jobs, for example?

Our estimation and conclusions

We estimate that the prevalence of multi-payroll work among 16–64-year-olds in Great Britain was between 18 and 20 per cent for women and between 14 and 16 per cent for men in 2018–19. These figures are much higher than those reported in official labour market statistics but notably close to Scott et al.’s (2020) estimate of second earnings in the USA (18 per cent), despite our exclusion of the ‘hidden economy’.

Whether including the hidden economy or all formal employment activities, however short in duration and without individual response bias, multiple jobbing seems much better captured here than in official social surveys. While our multi-payroll measure is not without limitations, it strengthens the case for recognising multiple job holding as a significant feature of contemporary labour markets.

Data acknowledgement: Office for National Statistics; His Majesty’s Revenue and Customs, released 01 August 2024, ONS SRS Metadata Catalogue, dataset, Annual Survey of Hours and Earnings linked to PAYE and Self-Assessment data – GB, https://doi.org/10.57906/566k-5q15

Funding: This work is supported by ADR UK (Administrative Data Research UK), an investment of the UK Economic and Social Research Council (ESRC) (part of UK Research and Innovation). [Grant number: ES/Z502406/1].

Disclaimer: This work was undertaken in the Office for National Statistics Secure Research Service using data from ONS and other owners and does not imply the endorsement of the ONS or other data owners.

Darja Reuschke is an Associate Professor in the Department of Strategy and International Business at the University of Birmingham. Her research concerns changes in local labour markets and new forms and locations of work and businesses that are emerging through new technologies, economic restructuring, crisis and social change. She has recently been working on multiple jobholding in the UK using payroll data.

Tracey Warren Is Professor of Sociology at the Nottingham University Business School and internationally recognised for her research on working lives. Her areas of research expertise include job quality, underemployment, work time, work-life balance, atypical working, paid and unpaid labour, equality, diversity and inclusion in work, and employment in/equalities.

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Better data do not necessarily produce better knowledge. Time Use Surveys and an innovative sequence method, Dynamic Hamming Matching (DHM), have transformed our capacity to examine the organisation of work time in far greater detail, recording work hours across the day and workdays across the week rather than relying on people’s estimates. Yet, despite these advances, women’s working time remains unclear. I came to this literature with what seemed like a simple question: when do women work? The problem was not simply one of data availability. It was also a set of analytical conventions that continue to position men’s working time as the standard against which women’s is compared, and treat pooled, gender-comparative analysis as the neutral starting point. This piece examines three moments from my own research on women’s work schedules, where those unreflective habits become visible, and argues that focusing analysis on women – as economic actors in their own right – is not a narrowing of enquiry but what the question demands.

‘A typical female pattern’

A study using DHM to identify a typology of work schedules in Belgium labelled one schedule a ‘typical female pattern’ because 68.5 per cent of those working it were women. The label sounds straightforward, but it is not.

A schedule can be majority female without being typical among women, and these are entirely different claims. The label tells us about the composition of the cluster, not about what women characteristically do. It tells us who works that schedule without telling us which schedule is most prevalent among women. Despite incorporating gender into the analysis, the study leaves a critical empirical question unanswered: when do women actually work?

This reflects a recurring feature of much time use research. Gender is included as a variable in models, and noted in findings, but it does not necessarily shape the questions being asked or how results are interpreted. Including gender and being attentive to the gendered organisation of work time are not one and the same.

‘Women continue to catch up with men’

A second study concludes that women are catching up with men in the amount of time spent in paid work. The phrase sounds neutral, even positive. But it subtly reveals assumptions that require scrutiny.

This interpretation implicitly frames men as the standard against which women are measured and towards which women must progress. Women’s work time is evaluated in relation to men’s. The question of what women’s work time looks like on its own terms is not entertained.

Comparison is a powerful analytical tool and much valuable research on gender inequality has followed from this approach. But comparison is not neutral. It directs attention towards gaps and convergence and away from variation within groups. When the comparison is always women against men, the analytical frame risks reproducing the very hierarchy it intends to critique. Comparison often means the male norm is left intact and the language is used to imply progress.

‘One might wonder whether a one-hour difference is that important?’

A reviewer queried whether workdays ending at 5pm were meaningfully different from those finishing at 6pm. From a modelling perspective, the question made sense. Parsimony is a defensible aim. But from the perspective of anyone responsible for collecting a young child from nursery and then doing the tea, bath and bed routine, the question had an obvious answer.

Subsequent analysis showed that this small difference in clock time corresponded to a significant difference in how work and care can be organised. Women with young children were more likely to have a workday schedule that finished at the earlier time than women without children. One work schedule makes the evening care routine possible; the other, for many women, does not.

Decisions about which distinctions to preserve and which to collapse when settling on a final typology rely on a tacit sense of what differences matter, and that sense is grounded in social position and experience. Who the knower is shapes what is identified as a meaningful distinction. Claims to neutral analysis can obscure the gendered standpoints built into apparently technical decisions. In the name of parsimony, we risk erasing precisely those distinctions that matter most for understanding how work is organised and for whom.

The politics of the pooled sample

Taken together, these three examples share a common characteristic of quantitative analyses of working time. In each, a methodological choice reflects a standpoint that passes as neutral: pooled data is often assumed to be the natural and neutral starting point, comparative analysis often leaves men’s work time as the implicit standard and positions women’s as deviations, and gender is treated as addressed once it has been included as a variable.

My response was to analyse women workers only. In many ways, this was a straightforward methodological decision: to define a population and examine how work is organised for that group. Subsetting a nationally representative survey to women does not undermine validity or generalisability: it produces a nationally representative sample of women and defines the population to which the analysis speaks.

However, I was warned that departing from convention would mean repeatedly having to answer the questions ‘why just women?’, ‘does this not narrow the scope of the research?’, ‘does this not paint a partial picture of the organisation of work time?’, and ‘isn’t it a bit too feminist?’. Tellingly, time use research on unpaid work routinely examines mothers and fathers separately, yet the parallel move in the study of paid working time still must answer for itself. After all, a ‘worker’ is assumed to refer to a rational, neutral economic actor. However, restricting analysis to women – or to men – within a nationally representative survey is not a partial or lesser form of enquiry. It is often exactly what is needed to understand how work is organised for that group.

Time Use Survey data and more sophisticated methods are necessary but will not deepen our understanding of the organisation of work time if the questions guiding their use continue to take men’s working patterns as the norm. I argue that the problem is not that focusing on women is too political, but that analysing pooled data is treated as if it were not political at all.

Jennifer Whillans is Senior Lecturer in the School of Sociology, Politics and International Studies at the University of Bristol. She is a mixed methods sociologist interested in the temporal organisation of people and practices in daily life, with a particular focus on gendered use and experience of time. She is concerned with inter-practice connections such that participation in any given practice shapes, and is shaped by, participation in a repertoire of other practices.

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